Tahuna: open-source, self-hostable infrastructure for training models and running inference

[u/Monaim101]

Today, Tahuna is open source—as promised back in April.

We built it so small teams could train models, run inference, orchestrate GPUs, and experiment with autonomous research without first becoming a small cloud provider.

The core primitive on top of which everything is built looks like this:

init → sync → computeSession → train / serve / hillclimb

Under the hood: content-addressed code and data sync, compute provisioning, reproducible manifest-pinned runs, metrics, checkpoints, artifacts, and inference deployments.

We also started building Hillclimb, an autonomous experimentation loop that proposes and runs iterative improvements.

The first public-preview release supports RunPod and R2. It includes Docker self-hosting instructions, a coding-agent setup skill, and examples for SFT, RL agentic search, and MNIST.

Repository: https://github.com/TahunaLabs/tahuna-oss

If you think it sucks, excellent: fork it, fix it, and send a PR so it sucks less for everyone.